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Predicting cancer relapse following lung stereotactic radiotherapy: an external validation study using real-world evidence

PURPOSE: For patients receiving lung stereotactic ablative radiotherapy (SABR), evidence suggests that high peritumor density predicts an increased risk of microscopic disease (MDE) and local-regional failure, but only if there is low or heterogenous incidental dose surrounding the tumor (GTV). A da...

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Autores principales: Davey, Angela, Thor, Maria, van Herk, Marcel, Faivre-Finn, Corinne, Rimner, Andreas, Deasy, Joseph O., McWilliam, Alan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10369005/
https://www.ncbi.nlm.nih.gov/pubmed/37503315
http://dx.doi.org/10.3389/fonc.2023.1156389
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author Davey, Angela
Thor, Maria
van Herk, Marcel
Faivre-Finn, Corinne
Rimner, Andreas
Deasy, Joseph O.
McWilliam, Alan
author_facet Davey, Angela
Thor, Maria
van Herk, Marcel
Faivre-Finn, Corinne
Rimner, Andreas
Deasy, Joseph O.
McWilliam, Alan
author_sort Davey, Angela
collection PubMed
description PURPOSE: For patients receiving lung stereotactic ablative radiotherapy (SABR), evidence suggests that high peritumor density predicts an increased risk of microscopic disease (MDE) and local-regional failure, but only if there is low or heterogenous incidental dose surrounding the tumor (GTV). A data-mining method (Cox-per-radius) has been developed to investigate this dose-density interaction. We apply the method to predict local relapse (LR) and regional failure (RF) in patients with non-small cell lung cancer. METHODS: 199 patients treated in a routine setting were collated from a single institution for training, and 76 patients from an external institution for validation. Three density metrics (mean, 90(th) percentile, standard deviation (SD)) were studied in 1mm annuli between 0.5cm inside and 2cm outside the GTV boundary. Dose SD and fraction of volume receiving less than 30Gy were studied in annuli 0.5-2cm outside the GTV to describe incidental MDE dosage. Heat-maps were created that correlate with changes in LR and RF rates due to the interaction between dose heterogeneity and density at each distance combination. Regions of significant improvement were studied in Cox proportional hazards models, and explored with and without re-fitting in external data. Correlations between the dose component of the interaction and common dose metrics were reported. RESULTS: Local relapse occurred at a rate of 6.5% in the training cohort, and 18% in the validation cohort, which included larger and more centrally located tumors. High peritumor density in combination with high dose variability (0.5 - 1.6cm) predicts LR. No interactions predicted RF. The LR interaction improved the predictive ability compared to using clinical variables alone (optimism-adjusted C-index; 0.82 vs 0.76). Re-fitting model coefficients in external data confirmed the importance of this interaction (C-index; 0.86 vs 0.76). Dose variability in the 0.5-1.6 cm annular region strongly correlates with heterogeneity inside the target volume (SD; ρ = 0.53 training, ρ = 0.65 validation). CONCLUSION: In these real-world cohorts, the combination of relatively high peritumor density and high dose variability predicts increase in LR, but not RF, following lung SABR. This external validation justifies potential use of the model to increase low-dose CTV margins for high-risk patients.
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spelling pubmed-103690052023-07-27 Predicting cancer relapse following lung stereotactic radiotherapy: an external validation study using real-world evidence Davey, Angela Thor, Maria van Herk, Marcel Faivre-Finn, Corinne Rimner, Andreas Deasy, Joseph O. McWilliam, Alan Front Oncol Oncology PURPOSE: For patients receiving lung stereotactic ablative radiotherapy (SABR), evidence suggests that high peritumor density predicts an increased risk of microscopic disease (MDE) and local-regional failure, but only if there is low or heterogenous incidental dose surrounding the tumor (GTV). A data-mining method (Cox-per-radius) has been developed to investigate this dose-density interaction. We apply the method to predict local relapse (LR) and regional failure (RF) in patients with non-small cell lung cancer. METHODS: 199 patients treated in a routine setting were collated from a single institution for training, and 76 patients from an external institution for validation. Three density metrics (mean, 90(th) percentile, standard deviation (SD)) were studied in 1mm annuli between 0.5cm inside and 2cm outside the GTV boundary. Dose SD and fraction of volume receiving less than 30Gy were studied in annuli 0.5-2cm outside the GTV to describe incidental MDE dosage. Heat-maps were created that correlate with changes in LR and RF rates due to the interaction between dose heterogeneity and density at each distance combination. Regions of significant improvement were studied in Cox proportional hazards models, and explored with and without re-fitting in external data. Correlations between the dose component of the interaction and common dose metrics were reported. RESULTS: Local relapse occurred at a rate of 6.5% in the training cohort, and 18% in the validation cohort, which included larger and more centrally located tumors. High peritumor density in combination with high dose variability (0.5 - 1.6cm) predicts LR. No interactions predicted RF. The LR interaction improved the predictive ability compared to using clinical variables alone (optimism-adjusted C-index; 0.82 vs 0.76). Re-fitting model coefficients in external data confirmed the importance of this interaction (C-index; 0.86 vs 0.76). Dose variability in the 0.5-1.6 cm annular region strongly correlates with heterogeneity inside the target volume (SD; ρ = 0.53 training, ρ = 0.65 validation). CONCLUSION: In these real-world cohorts, the combination of relatively high peritumor density and high dose variability predicts increase in LR, but not RF, following lung SABR. This external validation justifies potential use of the model to increase low-dose CTV margins for high-risk patients. Frontiers Media S.A. 2023-07-12 /pmc/articles/PMC10369005/ /pubmed/37503315 http://dx.doi.org/10.3389/fonc.2023.1156389 Text en Copyright © 2023 Davey, Thor, van Herk, Faivre-Finn, Rimner, Deasy and McWilliam https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Davey, Angela
Thor, Maria
van Herk, Marcel
Faivre-Finn, Corinne
Rimner, Andreas
Deasy, Joseph O.
McWilliam, Alan
Predicting cancer relapse following lung stereotactic radiotherapy: an external validation study using real-world evidence
title Predicting cancer relapse following lung stereotactic radiotherapy: an external validation study using real-world evidence
title_full Predicting cancer relapse following lung stereotactic radiotherapy: an external validation study using real-world evidence
title_fullStr Predicting cancer relapse following lung stereotactic radiotherapy: an external validation study using real-world evidence
title_full_unstemmed Predicting cancer relapse following lung stereotactic radiotherapy: an external validation study using real-world evidence
title_short Predicting cancer relapse following lung stereotactic radiotherapy: an external validation study using real-world evidence
title_sort predicting cancer relapse following lung stereotactic radiotherapy: an external validation study using real-world evidence
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10369005/
https://www.ncbi.nlm.nih.gov/pubmed/37503315
http://dx.doi.org/10.3389/fonc.2023.1156389
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